Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 KiB
Average record size in memory112.5 B

Variable types

Numeric11
Categorical3

Alerts

year has constant value "2012" Constant
BUI is highly overall correlated with DC and 7 other fieldsHigh correlation
DC is highly overall correlated with BUI and 6 other fieldsHigh correlation
DMC is highly overall correlated with BUI and 8 other fieldsHigh correlation
FFMC is highly overall correlated with BUI and 7 other fieldsHigh correlation
FWI is highly overall correlated with BUI and 7 other fieldsHigh correlation
ISI is highly overall correlated with BUI and 7 other fieldsHigh correlation
RH is highly overall correlated with DMC and 4 other fieldsHigh correlation
Rain is highly overall correlated with BUI and 5 other fieldsHigh correlation
Temperature is highly overall correlated with BUI and 6 other fieldsHigh correlation
day is highly overall correlated with BUI and 1 other fieldsHigh correlation
Rain has 133 (54.7%) zeros Zeros
ISI has 4 (1.6%) zeros Zeros
FWI has 9 (3.7%) zeros Zeros

Reproduction

Analysis started2025-06-05 04:00:41.349101
Analysis finished2025-06-05 04:00:47.767985
Duration6.42 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

day
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.761317
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:47.810299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8425522
Coefficient of variation (CV)0.56102877
Kurtosis-1.2056
Mean15.761317
Median Absolute Deviation (MAD)8
Skewness0.00036459881
Sum3830
Variance78.190729
MonotonicityNot monotonic
2025-06-05T09:30:47.865428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 8
 
3.3%
2 8
 
3.3%
3 8
 
3.3%
4 8
 
3.3%
5 8
 
3.3%
6 8
 
3.3%
7 8
 
3.3%
8 8
 
3.3%
9 8
 
3.3%
10 8
 
3.3%
Other values (21) 163
67.1%
ValueCountFrequency (%)
1 8
3.3%
2 8
3.3%
3 8
3.3%
4 8
3.3%
5 8
3.3%
6 8
3.3%
7 8
3.3%
8 8
3.3%
9 8
3.3%
10 8
3.3%
ValueCountFrequency (%)
31 4
1.6%
30 8
3.3%
29 8
3.3%
28 8
3.3%
27 8
3.3%
26 8
3.3%
25 8
3.3%
24 8
3.3%
23 8
3.3%
22 8
3.3%

month
Categorical

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
8
62 
7
61 
6
60 
9
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Length

2025-06-05T09:30:47.924946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-05T09:30:47.966214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring characters

ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 62
25.5%
7 61
25.1%
6 60
24.7%
9 60
24.7%

year
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2012
243 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012 243
100.0%

Length

2025-06-05T09:30:48.018307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-05T09:30:48.039003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2012 243
100.0%

Most occurring characters

ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 486
50.0%
0 243
25.0%
1 243
25.0%

Temperature
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.152263
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.080388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.9
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6280395
Coefficient of variation (CV)0.11283932
Kurtosis-0.14141446
Mean32.152263
Median Absolute Deviation (MAD)3
Skewness-0.19132733
Sum7813
Variance13.16267
MonotonicityNot monotonic
2025-06-05T09:30:48.135442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35 29
11.9%
31 25
10.3%
34 24
9.9%
33 23
9.5%
30 22
9.1%
36 21
8.6%
32 21
8.6%
29 18
7.4%
28 15
6.2%
27 8
 
3.3%
Other values (9) 37
15.2%
ValueCountFrequency (%)
22 2
 
0.8%
24 3
 
1.2%
25 6
 
2.5%
26 5
 
2.1%
27 8
 
3.3%
28 15
6.2%
29 18
7.4%
30 22
9.1%
31 25
10.3%
32 21
8.6%
ValueCountFrequency (%)
42 1
 
0.4%
40 3
 
1.2%
39 6
 
2.5%
38 3
 
1.2%
37 8
 
3.3%
36 21
8.6%
35 29
11.9%
34 24
9.9%
33 23
9.5%
32 21
8.6%

RH
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.041152
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.202921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152.5
median63
Q373.5
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.82816
Coefficient of variation (CV)0.23900523
Kurtosis-0.50894281
Mean62.041152
Median Absolute Deviation (MAD)11
Skewness-0.24279046
Sum15076
Variance219.87433
MonotonicityNot monotonic
2025-06-05T09:30:48.278473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 10
 
4.1%
55 10
 
4.1%
78 8
 
3.3%
54 8
 
3.3%
58 8
 
3.3%
73 7
 
2.9%
80 7
 
2.9%
66 7
 
2.9%
68 7
 
2.9%
65 7
 
2.9%
Other values (52) 164
67.5%
ValueCountFrequency (%)
21 1
 
0.4%
24 1
 
0.4%
26 1
 
0.4%
29 1
 
0.4%
31 1
 
0.4%
33 2
0.8%
34 3
1.2%
35 1
 
0.4%
36 1
 
0.4%
37 3
1.2%
ValueCountFrequency (%)
90 1
 
0.4%
89 3
1.2%
88 3
1.2%
87 4
1.6%
86 3
1.2%
84 2
 
0.8%
83 1
 
0.4%
82 3
1.2%
81 6
2.5%
80 7
2.9%

Ws
Real number (ℝ)

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.493827
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.339128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8113853
Coefficient of variation (CV)0.18145196
Kurtosis2.6217035
Mean15.493827
Median Absolute Deviation (MAD)2
Skewness0.55558584
Sum3765
Variance7.9038874
MonotonicityNot monotonic
2025-06-05T09:30:48.390284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
14 43
17.7%
15 40
16.5%
13 30
12.3%
17 28
11.5%
16 27
11.1%
18 25
10.3%
19 15
 
6.2%
21 8
 
3.3%
11 7
 
2.9%
12 7
 
2.9%
Other values (8) 13
 
5.3%
ValueCountFrequency (%)
6 1
 
0.4%
8 1
 
0.4%
9 2
 
0.8%
10 3
 
1.2%
11 7
 
2.9%
12 7
 
2.9%
13 30
12.3%
14 43
17.7%
15 40
16.5%
16 27
11.1%
ValueCountFrequency (%)
29 1
 
0.4%
26 1
 
0.4%
22 2
 
0.8%
21 8
 
3.3%
20 2
 
0.8%
19 15
 
6.2%
18 25
10.3%
17 28
11.5%
16 27
11.1%
15 40
16.5%

Rain
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76296296
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.444497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.37
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.0032068
Coefficient of variation (CV)2.6255623
Kurtosis25.822987
Mean0.76296296
Median Absolute Deviation (MAD)0
Skewness4.5686298
Sum185.4
Variance4.0128375
MonotonicityNot monotonic
2025-06-05T09:30:48.509971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.7 6
 
2.5%
0.6 6
 
2.5%
0.5 5
 
2.1%
1.8 3
 
1.2%
1.1 3
 
1.2%
Other values (29) 40
 
16.5%
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.5 5
 
2.1%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.8 2
 
0.8%
0.9 1
 
0.4%
ValueCountFrequency (%)
16.8 1
0.4%
13.1 1
0.4%
10.1 1
0.4%
8.7 1
0.4%
8.3 1
0.4%
7.2 1
0.4%
6.5 1
0.4%
6 1
0.4%
5.8 1
0.4%
4.7 1
0.4%

FFMC
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.842387
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.567577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.13
Q171.85
median83.3
Q388.3
95-th percentile92.19
Maximum96
Range67.4
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation14.349641
Coefficient of variation (CV)0.18434226
Kurtosis1.040087
Mean77.842387
Median Absolute Deviation (MAD)5.8
Skewness-1.3201301
Sum18915.7
Variance205.9122
MonotonicityNot monotonic
2025-06-05T09:30:48.631969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.9 7
 
2.9%
89.4 5
 
2.1%
89.3 4
 
1.6%
85.4 4
 
1.6%
89.1 4
 
1.6%
88.1 3
 
1.2%
78.3 3
 
1.2%
47.4 3
 
1.2%
88.3 3
 
1.2%
79.9 3
 
1.2%
Other values (163) 204
84.0%
ValueCountFrequency (%)
28.6 1
0.4%
30.5 1
0.4%
36.1 1
0.4%
37.3 1
0.4%
37.9 1
0.4%
40.9 1
0.4%
41.1 1
0.4%
42.6 1
0.4%
44.9 1
0.4%
45 1
0.4%
ValueCountFrequency (%)
96 1
0.4%
94.3 1
0.4%
94.2 1
0.4%
93.9 2
0.8%
93.8 1
0.4%
93.7 1
0.4%
93.3 1
0.4%
93 1
0.4%
92.5 2
0.8%
92.2 2
0.8%

DMC
Real number (ℝ)

High correlation 

Distinct165
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.680658
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.701020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.8
95-th percentile41.04
Maximum65.9
Range65.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.39304
Coefficient of variation (CV)0.84417465
Kurtosis2.462551
Mean14.680658
Median Absolute Deviation (MAD)6.9
Skewness1.5229829
Sum3567.4
Variance153.58743
MonotonicityNot monotonic
2025-06-05T09:30:48.769551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 5
 
2.1%
12.5 4
 
1.6%
1.9 4
 
1.6%
2.5 3
 
1.2%
3.4 3
 
1.2%
2.6 3
 
1.2%
3 3
 
1.2%
1.3 3
 
1.2%
5.8 3
 
1.2%
4.6 3
 
1.2%
Other values (155) 209
86.0%
ValueCountFrequency (%)
0.7 1
 
0.4%
0.9 2
0.8%
1.1 2
0.8%
1.2 1
 
0.4%
1.3 3
1.2%
1.7 1
 
0.4%
1.9 4
1.6%
2.1 1
 
0.4%
2.2 2
0.8%
2.4 1
 
0.4%
ValueCountFrequency (%)
65.9 1
0.4%
61.3 1
0.4%
56.3 1
0.4%
54.2 1
0.4%
51.3 1
0.4%
50.2 1
0.4%
47 1
0.4%
46.6 1
0.4%
46.1 1
0.4%
45.6 1
0.4%

DC
Real number (ℝ)

High correlation 

Distinct197
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.430864
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.828870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q112.35
median33.1
Q369.1
95-th percentile158.94
Maximum220.4
Range213.5
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation47.665606
Coefficient of variation (CV)0.96428834
Kurtosis1.5964668
Mean49.430864
Median Absolute Deviation (MAD)23.9
Skewness1.4734602
Sum12011.7
Variance2272.01
MonotonicityNot monotonic
2025-06-05T09:30:48.891866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5
 
2.1%
7.8 4
 
1.6%
8.3 4
 
1.6%
7.6 4
 
1.6%
8.4 4
 
1.6%
7.5 4
 
1.6%
8.2 4
 
1.6%
17 3
 
1.2%
10 2
 
0.8%
7.4 2
 
0.8%
Other values (187) 207
85.2%
ValueCountFrequency (%)
6.9 1
 
0.4%
7 2
0.8%
7.1 1
 
0.4%
7.3 2
0.8%
7.4 2
0.8%
7.5 4
1.6%
7.6 4
1.6%
7.7 2
0.8%
7.8 4
1.6%
7.9 1
 
0.4%
ValueCountFrequency (%)
220.4 1
0.4%
210.4 1
0.4%
200.2 1
0.4%
190.6 1
0.4%
181.3 1
0.4%
180.4 1
0.4%
177.3 1
0.4%
171.3 1
0.4%
168.2 1
0.4%
167.2 1
0.4%

ISI
Real number (ℝ)

High correlation  Zeros 

Distinct106
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7423868
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:48.958732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.25
95-th percentile13.38
Maximum19
Range19
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.1542338
Coefficient of variation (CV)0.87597954
Kurtosis0.86232522
Mean4.7423868
Median Absolute Deviation (MAD)2.4
Skewness1.1402426
Sum1152.4
Variance17.257659
MonotonicityNot monotonic
2025-06-05T09:30:49.021533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
3.3%
1.2 7
 
2.9%
0.4 5
 
2.1%
5.6 5
 
2.1%
4.7 5
 
2.1%
5.2 5
 
2.1%
2.8 5
 
2.1%
1.5 5
 
2.1%
1 5
 
2.1%
1.3 4
 
1.6%
Other values (96) 189
77.8%
ValueCountFrequency (%)
0 4
1.6%
0.1 4
1.6%
0.2 4
1.6%
0.3 3
1.2%
0.4 5
2.1%
0.5 2
 
0.8%
0.6 4
1.6%
0.7 4
1.6%
0.8 3
1.2%
0.9 2
 
0.8%
ValueCountFrequency (%)
19 1
0.4%
18.5 1
0.4%
17.2 1
0.4%
16.6 1
0.4%
16 1
0.4%
15.7 2
0.8%
15.5 1
0.4%
14.3 1
0.4%
14.2 1
0.4%
13.8 2
0.8%

BUI
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.690535
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:49.085696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.42
Q16
median12.4
Q322.65
95-th percentile46.4
Maximum68
Range66.9
Interquartile range (IQR)16.65

Descriptive statistics

Standard deviation14.228421
Coefficient of variation (CV)0.85248443
Kurtosis1.9560166
Mean16.690535
Median Absolute Deviation (MAD)7.3
Skewness1.4527448
Sum4055.8
Variance202.44797
MonotonicityNot monotonic
2025-06-05T09:30:49.148665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
2.1%
5.1 4
 
1.6%
10.9 3
 
1.2%
4.4 3
 
1.2%
3.9 3
 
1.2%
11.5 3
 
1.2%
2.9 3
 
1.2%
8.3 3
 
1.2%
14.2 3
 
1.2%
2.4 3
 
1.2%
Other values (163) 210
86.4%
ValueCountFrequency (%)
1.1 1
 
0.4%
1.4 2
0.8%
1.6 2
0.8%
1.7 2
0.8%
1.8 2
0.8%
2.2 1
 
0.4%
2.4 3
1.2%
2.6 2
0.8%
2.7 2
0.8%
2.8 2
0.8%
ValueCountFrequency (%)
68 1
0.4%
67.4 1
0.4%
64 1
0.4%
62.9 1
0.4%
59.5 1
0.4%
59.3 1
0.4%
57.1 1
0.4%
54.9 1
0.4%
54.7 1
0.4%
50.9 1
0.4%

FWI
Real number (ℝ)

High correlation  Zeros 

Distinct125
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0353909
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-05T09:30:49.225430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.2
Q311.45
95-th percentile21.53
Maximum31.1
Range31.1
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.4405677
Coefficient of variation (CV)1.0575912
Kurtosis0.65498526
Mean7.0353909
Median Absolute Deviation (MAD)3.8
Skewness1.1475925
Sum1709.6
Variance55.362048
MonotonicityNot monotonic
2025-06-05T09:30:49.292337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 12
 
4.9%
0.8 10
 
4.1%
0.5 9
 
3.7%
0 9
 
3.7%
0.1 9
 
3.7%
0.3 8
 
3.3%
0.9 7
 
2.9%
0.2 6
 
2.5%
0.7 5
 
2.1%
0.6 4
 
1.6%
Other values (115) 164
67.5%
ValueCountFrequency (%)
0 9
3.7%
0.1 9
3.7%
0.2 6
2.5%
0.3 8
3.3%
0.4 12
4.9%
0.5 9
3.7%
0.6 4
 
1.6%
0.7 5
2.1%
0.8 10
4.1%
0.9 7
2.9%
ValueCountFrequency (%)
31.1 1
0.4%
30.3 1
0.4%
30.2 1
0.4%
30 1
0.4%
26.9 1
0.4%
26.3 1
0.4%
26.1 1
0.4%
25.4 1
0.4%
24.5 1
0.4%
24 1
0.4%

Region
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
122 
1
121 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 122
50.2%
1 121
49.8%

Length

2025-06-05T09:30:49.352778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-05T09:30:49.519294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 122
50.2%
1 121
49.8%

Most occurring characters

ValueCountFrequency (%)
0 122
50.2%
1 121
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122
50.2%
1 121
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122
50.2%
1 121
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122
50.2%
1 121
49.8%

Interactions

2025-06-05T09:30:46.989110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-05T09:30:46.396223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-05T09:30:46.942054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-05T09:30:49.560682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
WsBUIDCDMCFFMCFWIISIRHRainRegionTemperaturedaymonth
Ws1.0000.0270.0600.001-0.0670.0340.0320.2010.0110.262-0.2240.0710.122
BUI0.0271.0000.9430.9880.8070.9110.811-0.467-0.5760.0000.5860.5170.326
DC0.0600.9431.0000.8930.7350.8490.746-0.347-0.6120.0800.5050.4800.281
DMC0.0010.9880.8931.0000.8220.9160.822-0.505-0.5590.1150.6110.5030.330
FFMC-0.0670.8070.7350.8221.0000.9680.989-0.665-0.7410.2130.6660.2510.260
FWI0.0340.9110.8490.9160.9681.0000.975-0.598-0.7180.0920.6570.3470.271
ISI0.0320.8110.7460.8220.9890.9751.000-0.643-0.7380.2420.6480.2390.245
RH0.201-0.467-0.347-0.505-0.665-0.598-0.6431.0000.1790.421-0.643-0.0900.223
Rain0.011-0.576-0.612-0.559-0.741-0.718-0.7380.1791.0000.082-0.293-0.1700.089
Region0.2620.0000.0800.1150.2130.0920.2420.4210.0821.0000.3160.0000.000
Temperature-0.2240.5860.5050.6110.6660.6570.648-0.643-0.2930.3161.0000.1230.391
day0.0710.5170.4800.5030.2510.3470.239-0.090-0.1700.0000.1231.0000.000
month0.1220.3260.2810.3300.2600.2710.2450.2230.0890.0000.3910.0001.000

Missing values

2025-06-05T09:30:47.537675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-05T09:30:47.728173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIRegion
01620122957180.065.73.47.61.33.40.50
12620122961131.364.44.17.61.03.90.40
236201226822213.147.12.57.10.32.70.10
34620122589132.528.61.36.90.01.70.00
45620122777160.064.83.014.21.23.90.50
56620123167140.082.65.822.23.17.02.50
67620123354130.088.29.930.56.410.97.20
78620123073150.086.612.138.35.613.57.10
89620122588130.252.97.938.80.410.50.30
910620122879120.073.29.546.31.312.60.90
daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIRegion
23321920123534170.092.223.697.313.829.421.61
23422920123364130.088.926.1106.37.132.413.71
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